With continuous technology scaling, the power density and hence the temperature of Network-on-Chip (NoC) may increase rapidly. This in-turn degrades the performance of the chip and increases the chances of creating thermal hot-spots. Task allocation and scheduling (TAS) in NoC-based Multiprocessor Systems-on-Chip have significant effects on the energy consumption of the chip and the finish time of the application. Temperature profile of a chip depends on the power consumptions of the tiles and their relative positions. In this paper, we have proposed a simulated annealing based thermal-aware Task Allocation and Scheduling (TAS) method which jointly optimizes the task to core allocation and task-scheduling problem for the periodic real-time applications. It is a platform-based TAS procedure and is applicable for the Networks-on-Chip (NoCs) containing both the homogeneous and heterogeneous cores. Along with temperature minimization, our proposed method has also been applied with the objective of minimizing the finish time of the application. The trade-off between the application finish time and the peak temperature of the chip has also been analyzed in this work. An integer linear programming formulation for the TAS problem, mentioned in a recent literature, has been adopted to evaluate the accuracy of the solutions provided by our proposed method. We have also compared our method with a thermal-aware TAS technique proposed in a recent literature and found $$12.74\%$$12.74% and $$35.06\%$$35.06% improvements in the finish time of the application and the peak temperature of the chip respectively for a fully heterogeneous NoC-platform.

中文翻译：

---

基于网格的异构 NoC 中周期性实时应用的热感知任务分配和调度

随着技术的不断扩展，功率密度以及片上网络 (NoC) 的温度可能会迅速增加。这反过来会降低芯片的性能并增加产生热点的机会。基于 NoC 的多处理器片上系统中的任务分配和调度 (TAS) 对芯片的能耗和应用程序的完成时间有显着影响。芯片的温度分布取决于瓦片的功耗及其相对位置。在本文中，我们提出了一种基于模拟退火的热感知任务分配和调度（TAS）方法，该方法联合优化了任务到核心分配和周期性实时应用的任务调度问题。它是一个基于平台的 TAS 程序，适用于包含同构和异构核心的片上网络 (NoC)。随着温度最小化，我们提出的方法也已被应用，目的是最小化应用程序的完成时间。在这项工作中还分析了应用完成时间和芯片峰值温度之间的权衡。最近文献中提到的 TAS 问题的整数线性规划公式已被用于评估我们提出的方法提供的解决方案的准确性。我们还将我们的方法与最近文献中提出的热感知 TAS 技术进行了比较，发现 $$12.74\%$$$12.74% 和 $35.06\%$$35。